Systems and methods for prioritizing emergency calls

ABSTRACT

Systems for and methods of determining the priority of a call interaction include receiving a call interaction from a call center; validating, by a validation and transcription engine, that the call interaction is authentic; converting, by the validation and transcription engine, the call interaction into text; calculating, by the data calculation engine, a priority of the call interaction from the text and organization, location, and time information in the text by determining an important of words in the text and correlating the words to a priority class using a pre-trained algorithm that is trained on emergency-type and emergency services-type language; determining that the call interaction should be transmitted to the call center for initial handling by a call center agent; and transmitting the call interaction, the calculated priority, and the extracted information to the call center.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/837,094, filed Jun. 10, 2022, now allowed, which is a continuation ofU.S. patent application Ser. No. 17/197,742, filed Mar. 10, 2021, nowU.S. Pat. No. 11,375,063, the entire contents of each of which is herebyincorporated herein by express reference thereto.

TECHNICAL FIELD

The present disclosure relates to methods, apparatus, and systems toprioritize emergency calls received at a call center, and moreparticularly, to methods, apparatus, and systems that determine thepriority of a call interaction so that the most urgent call interactionsare answered first.

BACKGROUND

Today, calls to emergency services are typically handled using a simplequeue based on the time of the call (first in, first out). Once a callis answered, the dispatcher typically follows a set of rules forgathering case information and inputting the data manually by typing,which takes time. In addition, during the call, the dispatcher decidesthe case priority and must determine whether the information is valid.For some calls, however, every second is critical. Also, it is thedispatcher's responsibility to determine whether a call is valid or not.The response to such calls consumes valuable time and impairs thequality of service of the dispatcher.

The inability to quickly prioritize the load of incoming calls toemergency services during a standard work day can be fatal. This flaw ismagnified during disaster relief. The increase in calls to emergencyservices during a disaster often results in a long wait time, when inmany cases there is no time to wait.

Thus, there is a need for a solution that will allow emergency callcenter systems to create case information automatically and send theinformation immediately to the correct emergency service, withoutwaiting for a dispatcher, which decreases valuable time.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is best understood from the following detaileddescription when read with the accompanying figures. It is emphasizedthat, in accordance with the standard practice in the industry, variousfeatures are not drawn to scale. In fact, the dimensions of the variousfeatures may be arbitrarily increased or reduced for clarity ofdiscussion.

FIG. 1 is a simplified block diagram showing the different components ofthe call priority analysis system according to aspects of the presentdisclosure.

FIG. 2 is a flowchart of a method of determining the priority of a callinteraction according to embodiments of the present disclosure.

FIG. 3 is a block diagram of a computer system suitable for implementingone or more components in FIG. 1 according to one embodiment of thepresent disclosure.

DETAILED DESCRIPTION

This description and the accompanying drawings that illustrate aspects,embodiments, implementations, or applications should not be taken aslimiting—the claims define the protected invention. Various software,machine learning, mechanical, compositional, structural, electrical, andoperational changes may be made without departing from the spirit andscope of this description and the claims. In some instances, well-knownmachine logic, circuits, structures, or techniques have not been shownor described in detail as these are known to one of ordinary skill inthe art.

In this description, specific details are set forth describing someembodiments consistent with the present disclosure. Numerous specificdetails are set forth in order to provide a thorough understanding ofthe embodiments. It will be apparent, however, to one of ordinary skillin the art that some embodiments may be practiced without some or all ofthese specific details. The specific embodiments disclosed herein aremeant to be illustrative but not limiting. One of ordinary skill in theart may realize other elements that, although not specifically describedhere, are within the scope and the spirit of this disclosure. Inaddition, to avoid unnecessary repetition, one or more features shownand described in association with one embodiment may be incorporatedinto other embodiments unless specifically described otherwise or if theone or more features would make an embodiment non-functional.

The present disclosure describes systems and methods that process callinformation even before it reaches a call center agent (also referred toherein as a dispatcher) for emergency call centers and helps determinewhether or not a call interaction should reach an agent and the priorityin which a set of calls should reach an agent. In particular, thepresent systems and methods use algorithms to analyze and extract datafrom a call interaction for automatic prioritization and routing insteadof requiring a human agent to prioritize and route such callinteractions. In various embodiments, a validation algorithm, a speechto text algorithm, an entity identification algorithm, and aprioritization algorithm are used.

In several embodiments, when a call interaction is received at a callcenter, a call priority analysis system validates that the call isreliable, transcribes the call interaction from speech to text, extractsthe location of the call interaction, the case description, and thecaller details, calculates the priority of the call interactionaccording to a protocol or other set of rules, and determines whetherthe call interaction requires an agent handle and returns it to thequeue for follow up. In some embodiments, the call priority analysissystem performs these steps in real-time (e.g., the steps are performedas the data comes in). Advantageously, each call interaction isresponded to with shorter wait times, processed, saved, and givenattention in order of priority. Preferably, all emergency calls arehandled faster by de-prioritizing non-emergency calls.

In addition, handling of the call interaction is begun before it reachesan agent. The agent responds only to valid call interactions and only ifagent handling is needed. Moreover, the case information mayadvantageously be sent to the relevant emergency service automatically.

In certain embodiments, a combination of technologies is utilized. Forexample, speech to text algorithms may be used on incoming callinteractions, named entity recognition (NER) algorithms may be used toextract information from the call interaction before an agent receivesthe call interaction, and a gradient-boosted tree algorithm such as aneXtreme gradient boosting (XGBoost) algorithm may be used to implementcall prioritization. In various embodiments, the XGBoost algorithm ispre-trained on hand-crafted probabilistic grammar. The probabilisticgrammar preferably relates to emergency-type and emergency services-typelanguage.

The present systems and methods may provide several advantages. First,they can be used in emergency call centers to improve public safety. Itis well understood that lives are saved when emergency services canrespond more quickly to a critical incident, e.g., fire, active shootersituations, armed robbery, heart attack or stroke, or any other servicewith improved resolution when handled on an emergency basis. While themethods and systems described herein can be implemented in public safetysystems, it should be understood that they can also be used in any typeof call center to decrease call wait times and prioritize callconnections on other bases. Companies can build their organizationpriority by selecting important keywords, such as a product for sale,unsatisfied customers, clients that want to discontinue the service,customer loyalty, most recent customers, and more. Finally, the presentmethods and systems change the flow of a call interaction in a callcenter to ensure that the most important and urgent call interactionsare answered first (or for non-emergency call interactions, on any othersuitable basis). This is especially important when a natural disaster orother emergency occurs.

FIG. 1 is a simplified block diagram showing the different components ofthe call priority analysis system 100 according to various aspects ofthe present disclosure. FIG. 1 includes call center 150. The term “callcenter,” as used herein, can include any facility or system serversuitable for receiving and recording electronic call interactions fromcontacts. One such example is a call analytics center. Such callinteractions can include, for example, telephone calls, voice over IP(“VoIP”) and video. In various aspects, real-time interactions, such asvoice, video, or both, is preferably included. It is contemplated thatthese interactions may be transmitted by and through any type oftelecommunication device and over any medium suitable for carrying data.For example, the communications may be transmitted by or throughtelephone lines, cable, or wireless communications.

As shown, call center 150 includes a voice call processing unit 152. Thevoice call processing unit 152 is generally a hardware unit set in thecall center, such as an automatic call distributor (ACD) or adistributed private branch exchange (PBX), that receives callinteractions. An ACD distributes call interactions or tasks to agents.Generally, an ACD is part of a switching system designed to receive callinteractions and queue them. In one embodiment, the ACD is integratedwith a PBX switch, and directs call interactions to one of a pluralityof agent workstations. ACDs are specialized systems that are configuredto match call interactions to an available call center agent. ACDsgenerally receive incoming call interactions, determine where to route aparticular call interaction, and connect the call interaction to anavailable agent. For the purposes of the present disclosure, “ACD”refers to any combination of hardware, software and/or embedded logicthat is operable to automatically distribute incoming call interactions,including requests for service transmitted using any audio and/or videomeans, including signals, data or messages transmitted through voicedevices, text chat, web sessions, facsimile, instant messaging ande-mail.

Call priority analysis system 100 receives data from call center 150(e.g., from voice call processing unit 152) and processes the data todetermine the priority of a call interaction. As shown, call priorityanalysis system 100 includes validation and transcription engine 105,data calculation engine 110, and response processing service160.

Validation and transcription engine 105 includes validation algorithm115 and speech to text algorithm 125. Validation algorithm 115 takes acall interaction and determines whether or not the call interaction isauthentic or valid. For example, validation algorithm 115 can analyzethe call interaction for noises, laughter, or certain words. Laughtercan be evidence that the call interaction is not authentic and is apotential prank call. Speech to text algorithm 125 converts speech intotext by detecting, for example, words and phonemes. A speech signal isthe input for speech to text algorithm 125, and this speech signal isrepresented by an array of bytes. These bytes are usually arepresentation of speech samples, where in many cases 1 or 2 bytesrepresent a single sample. Speech to text algorithm 125 produces thecall transcription, represented by a string arrange (thephonetic/word-based textual representation of what has been said). Insome embodiments, validation algorithm 115 accepts the textualrepresentation and decides if the call interaction is a valid call to beprocessed, where the decision is represented by a boolean variable.

Data calculation engine 110 includes entity identification algorithm 120and prioritization algorithm 130. If valid, entity identificationalgorithm 120 accepts the textual representation and produces theinformative elements in the call interaction. The data structure to holdthis data is often a key (e.g., json, xml, dict, etc.), and this datastructure can be passed to external processes via an API. For example,when a fire is reported, the alarm in the fire department is firstcalled so the fire fighters will get ready, and then the call isfollowed up with an agent, so that precious time is not wasted. The datastructure is also passed to prioritization algorithm 130, whichdetermines the priority based on the internal artificial intelligence(AI) core.

According to various embodiments, entity identification algorithm 120classifies named entities mentioned in the text into pre-definedcategories such as organization names, locations, and time expressions.In some embodiments, prioritization algorithm 130 takes the text andmaps it to a class of priority. The priority is then passed to responseprocessing service 160. In certain embodiments, the priority isrepresented as an integer number, but can also be a string or a float.

Response processing service 160 receives data from call center 150(e.g., voice call processing unit 152) and data calculation engine 110.Based on the received data, response processing service 160 makes adecision whether the call interaction should be sent back to the callcenter queue for agent follow-up. The output of the response processingservice 160 can be uploaded to database 180 if needed, or can be sent toa user interface, call center, emergency service 190, dashboard,analysis tool, internal system, or external system via an applicationprogramming interface (API) or via streams. In various embodiments, theoutput includes one of more of the following: a timestamp, the casedescription, the priority, the location, the caller name, the callerphone number, and whether or not an agent is needed. As can be seen, thepriority can be outputted for various external processes, including butnot limited to the call center queue.

Referring now to FIG. 2 , a method 200 for determining the priority of acall interaction is described. At step 202, call priority analysissystem 100 receives a current call interaction from call center 150. Invarious embodiments, call center 150 is an emergency call center thatemploys emergency dispatchers. In certain embodiments, the current callinteraction includes emergency case location, the caller information,and the case description.

At step 204, call priority analysis system 100, via the validation andtranscription engine 105, validates that the current call interaction isauthentic and not a prank call. In various embodiments, validationalgorithm 115 determines whether the current call interaction is validor not. For example, validation algorithm 115 analyzes an amount ofnoise on the current call interaction, detects whether there is laughteron the current call interaction, evaluates words in the current callinteraction, or any combination thereof. Validation algorithm 115 actsas a filter layer, stopping irrelevant call interactions from beinganalyzed so that the system and method as a whole are more efficient andpriority interactions are expedited further as a result. In severalembodiments, the filtering of call interactions uses various modules,where each module is different, based on the validation. For example, avoice activity detector (VAD) can be used to detect the percentage ofnoise on the current call interaction or a laugh detection module can beused to detect the presence of laughter. The words used in the currentcall interaction may also be evaluated to determine if there are anywords triggering invalidity of the current call interaction. If thecurrent call interaction is not valid, the current call interaction doesnot proceed to the next step.

If the current call interaction is authentic, method 200 proceeds tostep 206, where call priority analysis system 100, via the speech totext algorithm 125, converts the current call interaction into text or atextual call transcription. In certain embodiments, the current callinteraction is converted to text in real-time. Although method 200illustrates that the current call interaction is first validated andthen converted to text, it should be understood that the current callinteraction may be first converted to text and then validated.

In various embodiments, the conversion of speech to text includesdetecting a word, a phoneme, or both, and is performed in a two-trackflow. For example, converting the current call interaction into text caninclude detecting a word, a phoneme, or both.

In some embodiments, the word-based track simply runs a comparison ofone to many, where the input is an audio segment, and this is convertedto a different representation that is then compared to a dictionary ofsuch representations. Other considerations, such as grammaticalstructure (e.g., “is” cannot be said twice in a row, and a verb,adjective, or noun is more likely to follow the term) may also beanalyzed. While this approach is generally accurate, there are more “outof dictionary” words since language is always evolving. The dictionaryneeds to be constantly updated, and sometimes it is difficult to keep upwith the change. This is the reason the phonetic-based track is alsogenerally used concurrently or sequentially to analyze the audio ortext.

In the phonetic-based track, the audio is converted to phonemes, where aphoneme is a short sound that is produced by the mouth and/or nose. Withthis approach, new words can be detected that do not exist in thedictionary using the phonetic transcription. Parallel to the grammaticalstructure, with this approach, a-priori statistics, namely probabilitiesof phonetic transitions are also considered.

Phonetic transitions are more tied to physical constraints. For example,there cannot be two continuous plosives (“p”+“p”). There has to be abreak between them for air to leave the mouth. After an audio segment isconverted into a phonetic transcription, another dictionary is used thatconverts them to the most probable textual representation (a term). Thisprocess can be done such that no matter what, a certain word will beproduced, even if it is a new word (for example “selfie,” which emergedin the past few years, can be created through this approach, forexample, “my BFF fell over the cliff while taking a selfie!”).

In both tracks, the words or phonemes can be extracted through eitherclassical machine learning (ML) approaches or more modern deep learning(DL) approaches.

Examples of classical ML approaches for word or phoneme detectioninclude Bassian approaches such as a hidden Markov model (HMM) withprobabilistic models, such as a Gaussian mixture model (GMM).Non-Bassian approaches include dynamic time warping (DTW) and generalpattern matching approaches.

Examples of modern DL approaches for word or phoneme detection includeuse of a recurrent neural network (RNN), a convolutional neural network(CNN), a gated recurrent unit (GRU), capsule networks, and residualnetworks. Often various layers of the different networks are combinedfor targeted applications.

At step 208, call priority analysis system 100, via entityidentification algorithm 120, extracts organization, location, and timeinformation from the text of the current call interaction. In certainembodiments, call priority analysis system 100 detects various entitiesfor documentation and automatic call handling.

In some embodiments, the transcription text is pre-processed to output anumeric representation for the words in the text. For example, for eachcall transcription, a pre-processing unit splits the text to words andcleans the text by removing punctuation, stop words, duplicative words,and plural words. In various embodiments, the pre-processing unit alsomaps each word to a numeric representation of the word. The numericrepresentation is a unique number or an embedding vector, which is avector of continuous numbers that represent a word's semantic meaning.

In some embodiments, extraction of organization, location, and timeinformation includes applying a named entity recognition (NER) model tothe text. The NER model identifies and categorizes key information(entities) in the text. An entity can be any word or series of wordsthat consistently refer to the same thing. Every detected entity isclassified into a predetermined category (e.g., organization, location,or time). The NER model, in various embodiments, classifies namedentities mentioned in transcription text into predefined categories suchas organization names, locations, and time expressions. For example, thenumeric representation for each word can be fed into a pre-trained NERmodel to obtain an entity tag or category for each word that is anorganization name, a location, or a time. In several embodiments, theNER model is pre-trained on deep convolutional and residual layers thatdetect and categorize the entity.

At step 210, call priority analysis system 100, via prioritizationalgorithm 130, calculates a priority of the current call interactionfrom the extracted information and the text. In various embodiments,calculating the priority is based on a protocol or set of rules. In someembodiments, call priority analysis system 100 saves the calculatedpriority and the extracted information in a database (e.g., database180). In certain embodiments, prioritization algorithm 130 classifiesthe given information into a priority class or level (e.g., high,medium, or low; 1, 2, or 3; or red, yellow, or green). The priority ofthe current call interaction is preferably set by the algorithmaccording to an agreed protocol.

In certain embodiments, prioritization algorithm 130 takes the text andmaps the text to a priority class (e.g., high, medium, or low). Thistype of task is called text classification in the natural languageunderstanding (NLU) domain. Examples of classical approaches to textclassification include use of support vector machines (SVM), logisticregression, and random forest algorithms. Examples of modern approachesto text classification include the use of XGBoost algorithms, longshort-term memory (LSTM) networks, bidirectional LSTM (BiLSTM) networks,and transformers.

In several embodiments, calculation of the priority of the current callinteraction includes first training a gradient-boosted tree algorithm(e.g., an XGBoost algorithm) to weigh an importance of the words in thetext and provide a priority class of a call interaction. In variousembodiments, calculation of the priority of the current call interactionincludes determining the importance of words in the text and correlatingthe words in the text to a priority class by using the trained model. Insome embodiments, calculation of the priority of the current callinteraction includes applying the weights learned and saved from thegradient-boosted tree algorithm to the words of the text. For example,the saved weights for the words that are found in the current callinteraction are provided to the trained gradient-boosted tree algorithmto output the priority class (e.g., 1, 2, or 3).

For example, on a set of previous call description texts that arerepresented as a set of token IDs as attributes, a pre-processing unitsplits the text to words and maps each word to a numeric representation(e.g., a unique ID or an embedding vector as described above). Thismapping is saved as a dictionary that maps a word to a number or anembedding vector. In various embodiments, a set of indicators for apriority class or a priority level (e.g., high, medium, or low; 1, 2, 3;or red, yellow, or green) is decided on and based on the emergencyservice policy.

Next, the numeric representation of the words from the text and previoustags from extracted information are provided to a feature extractor todetermine the weight or the importance of specific words. In certainembodiments, determining the importance of specific words in the textincludes applying term frequency-inverse document frequency (TFIDF) tothe text. TFIDF is a numerical statistic that reflects how important aword is in the text. TFIDF is calculated by multiplying two metrics: howmany times a word appears in the current text and the inverse documentfrequency of the word across all call description text. “TF” is thescoring of the frequency of the word in a current call description text,and “IDF” is a scoring of how rare the word is across all calldescription text. The TFIDF score highlights words that are distinct(content useful information) in a given text. In some cases, the moredistinct the word, the more weight or the more importance the word isgiven. Below is a preferred formula used to calculate TFIDF.

$w_{i,j} = {{tf}_{i,j} \times \log\left( \frac{N}{{df}_{i}} \right)}$

-   -   tf_(ij)=number of occurrences of i in j    -   df_(f)=number of documents containing i    -   N=total number of documents

The feature extractor calculates weights for words in order to providehigher weights to domain specific words, particularly words associatedmore with an emergency (or in non-emergency embodiments, other prioritywords like “long-standing customer”). For example, “dog” may have aweight of 0.3 while “fire” or “earthquake” may have a weight of 0.6.

Next, the normalized TFIDF vectors or weights are inserted to train agradient-boosted tree algorithm (e.g., an XGBoost model) to learnweights for the words. For example, the weight of the words are used asthe input for the XGBoost model, which uses the weight to predict apriority class. Thus, the weights of the words are correlated to one ormore priority classes. The “fit” function fits the gradient-boosted treealgorithm and learning the words' weights in respect to the priority(training phase of the algorithm). In various embodiments, the modelword weights are saved to calculate the priority of the current callinteraction and for research and monitoring of model performance.

In some embodiments, the trained gradient-boosted tree algorithm is usedto determine the priority of the current call interaction, and thetrained gradient-boosted tree algorithm is applied to the text of thecurrent call interaction. In various embodiments, the transcription textof the current call interaction is first pre-processed to output anumeric representation for the words (as explained above with respect tostep 208). In certain embodiments, a pre-processing unit loads adictionary (e.g., the dictionary from step 210) and maps each word to anumeric representation (e.g., a unique ID or an embedding vector) basedon the dictionary. In these embodiments, the gradient-boosted treealgorithm (e.g., XGBoost model) is applied to a new call interactiondirectly, such that no tags and no priority calculations or correlationsare needed.

At the end of the analysis process, all collected current callinteraction data is sent to response processing service 160 to createthe system response to the current call interaction. The system responsecontains the data needed for emergency services to respond, and can besent to the relevant emergency service (or services) immediately.

At step 212, call priority analysis system 100 determines that thecurrent call interaction should be transmitted to a queue of the callcenter for initial handling by a call center agent. In this case, adispatcher follow-up is needed. In other cases, the system response canbe used for display, dashboards, analytics, documentation, and/orrecords. Call priority analysis system 100 can also save the callinformation in a database for subsequent analysis, training of one ormore of the algorithms discussed herein, or official government, lawenforcement, or judicial uses or investigations.

At step 214, call priority analysis system 100 transmits the currentcall interaction, the calculated priority, and the extracted informationto the call center 150. In some embodiments, call priority analysissystem 100 transmits the calculated priority and the extractedinformation to emergency service 190.

Referring now to FIG. 3 , illustrated is a block diagram of a system 300suitable for implementing embodiments of the present disclosure,including call priority analysis system 100. System 300, such as part acomputer and/or a network server, includes a bus 302 or othercommunication mechanism for communicating information, whichinterconnects subsystems and components, including one or more of aprocessing component 304 (e.g., processor, micro-controller, digitalsignal processor (DSP), etc.), a system memory component 306 (e.g.,RAM), a static storage component 308 (e.g., ROM), a network interfacecomponent 312, a display component 314 (or alternatively, an interfaceto an external display), an input component 316 (e.g., keypad orkeyboard), and a cursor control component 318 (e.g., a mouse pad).

In accordance with embodiments of the present disclosure, system 300performs specific operations by processor 304 executing one or moresequences of one or more instructions contained in system memorycomponent 306. Such instructions may be read into system memorycomponent 306 from another computer readable medium, such as staticstorage component 308. These may include instructions to receive a callinteraction from a call center; validate, by a validation andtranscription engine, that the call interaction is authentic; convert,by the validation and transcription engine, the call interaction intotext; extract, by a data calculation engine, organization, location, andtime information from the text; calculate by the data calculationengine, a priority of the call interaction from the extractedinformation and the text; determine that the call interaction should betransmitted to a queue of the call center for initial handling by a callcenter agent; and transmit the call interaction, the calculatedpriority, and the extracted information to the call center.

In other embodiments, hard-wired circuitry may be used in place of or incombination with software instructions for implementation of one or moreembodiments of the disclosure.

Logic may be encoded in a computer readable medium, which may refer toany medium that participates in providing instructions to processor 304for execution. Such a medium may take many forms, including but notlimited to, non-volatile media, volatile media, and transmission media.In various implementations, volatile media includes dynamic memory, suchas system memory component 306, and transmission media includes coaxialcables, copper wire, and fiber optics, including wires that comprise bus302. Memory may be used to store visual representations of the differentoptions for searching or auto-synchronizing. In one example,transmission media may take the form of acoustic or light waves, such asthose generated during radio wave and infrared data communications. Somecommon forms of computer readable media include, for example, RAM, PROM,EPROM, FLASH-EPROM, any other memory chip or cartridge, carrier wave, orany other medium from which a computer is adapted to read.

In various embodiments of the disclosure, execution of instructionsequences to practice the disclosure may be performed by system 300. Invarious other embodiments, a plurality of systems 300 coupled bycommunication link 320 may perform instruction sequences to practice thedisclosure in coordination with one another. Computer system 300 maytransmit and receive messages, data, information and instructions,including one or more programs (i.e., application code) throughcommunication link 320 and communication interface 312. Received programcode may be executed by processor 304 as received and/or stored in diskdrive component 310 or some other non-volatile storage component forexecution.

The Abstract at the end of this disclosure is provided to comply with 37C.F.R. § 1.72(b) to allow a quick determination of the nature of thetechnical disclosure. It is submitted with the understanding that itwill not be used to interpret or limit the scope or meaning of theclaims.

What is claimed is:
 1. A call priority analysis system comprising: aprocessor and a computer readable medium operably coupled thereto, thecomputer readable medium comprising a plurality of instructions storedin association therewith that are accessible to, and executable by, theprocessor, to perform operations which comprise: receiving a callinteraction from a call center; validating, by a validation andtranscription engine, that the call interaction is authentic;converting, by the validation and transcription engine, the callinteraction into text; calculating, by a data calculation engine, apriority of the call interaction from the text and organization,location, and time information in the text, wherein calculating thepriority of the call interaction comprises determining an importance ofwords in the text and correlating the words in the text to a priorityclass by using a pre-trained algorithm that is trained on emergency-typeand emergency services-type language; determining that the callinteraction should be transmitted to the call center for initialhandling by a call center agent; and transmitting the call interaction,the calculated priority, and the organization, location, and timeinformation to the call center.
 2. The call priority analysis system ofclaim 1, wherein the pre-trained algorithm is pre-trained onhand-crafted probabilistic grammar.
 3. The call priority analysis systemof claim 1, wherein the pre-trained algorithm comprises a trained treealgorithm.
 4. The call priority analysis system of claim 3, wherein thetrained tree algorithm comprises a trained gradient-boosted treealgorithm.
 5. The call priority analysis system of claim 1, wherein theoperations further comprise saving the calculated priority and theorganization, location, and time information in a database.
 6. The callpriority analysis system of claim 5, wherein the operations furthercomprise extracting, by the data calculation engine, the organization,location, and time information from the text, wherein extracting theorganization, location, and time information comprises applying a namedentity recognition (NER) model to the text.
 7. The call priorityanalysis system of claim 1, wherein the operations further comprisetransmitting the calculated priority and the organization, location, andtime information to an emergency service, and wherein determining thatthe call interaction should be transmitted to the call center forinitial handling by a call center agent comprises determining that thecall interaction should be transmitted to a queue of the call center. 8.The call priority analysis system of claim 1, wherein validating thecall interaction comprises analyzing an amount of noise on the callinteraction, detecting whether there is laughter on the callinteraction, evaluating words in the call interaction, or anycombination thereof.
 9. The call priority analysis system of claim 1,wherein determining the importance of the words in the text comprisesapplying term frequency-inverse document frequency (TFIDF) to the text.10. A method of determining the priority of a call interaction, whichcomprises: receiving a call interaction from a call center; validating,by a validation and transcription engine, that the call interaction isauthentic; converting, by the validation and transcription engine, thecall interaction into text; calculating, by a data calculation engine, apriority of the call interaction from the text and organization,location, and time information in the text, wherein calculating thepriority of the call interaction comprises determining an importance ofwords in the text and correlating the words in the text to a priorityclass by using a pre-trained algorithm that is trained on emergency-typeand emergency services-type language; determining that the callinteraction should be transmitted to the call center for initialhandling by a call center agent; and transmitting the call interaction,the calculated priority, and the organization, location, and timeinformation to the call center.
 11. The method of claim 10, wherein thepre-trained algorithm is pre-trained on hand-crafted probabilisticgrammar.
 12. The method of claim 10, wherein the pre-trained algorithmcomprises a trained tree algorithm.
 13. The method of claim 12, whereinthe trained tree algorithm comprises a trained gradient-boosted treealgorithm.
 14. The method of claim 13, which further comprisesextracting, by the data calculation engine, the organization, location,and time information from the text, wherein extracting the organization,location, and time information comprises applying a named entityrecognition (NER) model to the text.
 15. The method of claim 10, whereindetermining the importance of the words in the text comprises applyingterm frequency-inverse document frequency (TFIDF) to the text.
 16. Anon-transitory computer-readable medium having stored thereoncomputer-readable instructions executable by a processor to performoperations which comprise: receiving a call interaction from a callcenter; validating, by a validation and transcription engine, that thecall interaction is authentic; converting, by the validation andtranscription engine, the call interaction into text; calculating, by adata calculation engine, a priority of the call interaction from thetext and organization, location, and time information in the text,wherein calculating the priority of the call interaction comprisesdetermining an importance of words in the text and correlating the wordsin the text to a priority class by using a pre-trained algorithm that istrained on emergency-type and emergency services-type language;determining that the call interaction should be transmitted to the callcenter for initial handling by a call center agent; and transmitting thecall interaction, the calculated priority, and the organization,location, and time information to the call center.
 17. Thenon-transitory computer-readable medium of claim 16, wherein thepre-trained algorithm is pre-trained on hand-crafted probabilisticgrammar.
 18. The non-transitory computer-readable medium of claim 16,wherein the pre-trained algorithm comprises a trained tree algorithm.19. The non-transitory computer-readable medium of claim 18, wherein thetrained tree algorithm comprises a trained gradient-boosted treealgorithm.
 20. The non-transitory computer-readable medium of claim 19,wherein the operations further comprise extracting, by the datacalculation engine, the organization, location, and time informationfrom the text, and wherein extracting the organization, location, andtime information comprises applying a named entity recognition (NER)model to the text.